Another year, another NRF Big Show in the books. This year marked my eleventh visit to New York City for retail’s biggest show. This appeared to be the biggest conference from any that I have attended, with some estimates suggesting 36,000 people were there. As in the past, the usual themes emerged: omni-channel, mobility, big data, etc. This year, there was a lot of buzz around retail transformation, and going beyond omni-channel to ensure an end-to-end, seamless approach to the customer journey. In this way, supply chain seemed to have more of a presence. There were even a number of robotics companies in attendance showcasing warehouse automation technology. Throughout my meetings and aisle wanderings, omni-channel returns and machine learning kept popping up.
Here are a few highlights from the show for me.
As e-commerce continues to grow, so too do returns. When these returns come in to a store, retailers need to get the item back in a selling channel as soon as possible, hopefully without having to mark the item down. Some studies have indicated that online orders are three times more likely to be returned than in-store purchases. For this reason, more and more retailers are making returns part of the forecasting process. According to my omni-channel returns management research, 57 percent of retailers make returns management part of the forecasting process.
Doddle is a parcel store network based in the UK. The company partners with more than 50 retail brands to offer click-and-collect and returns services in over 500 locations using Zebra Technologies’ scanners and printers. The interesting thing about Doddle is that the company enables returns both in-store and at drop-off boxes. Customers can pre-book their return using Facebook Messenger and a QR code is sent to the customer. At this point, they can simply attach it to the item they are returning and drop it off at one of Doddle’s locations. For in-store returns, the retailer can have a separate location for the returns. The customer can look up the order or scan the QR code and complete the return. Store can even send an email to the customer to let them know what times are busy in the store and what times are slower. For store associates, the code on the returned item can tell them whether to simply re-stock the item or ship it back to a DC.
Manhattan Associates is continuing to expand its Active Omni solution suite. The solution includes order management, point-of-sale (POS), and customer service. Manhattan has now rolled out digital self-service as part of the solution suite for order tracking. This allows consumers to ask Alexa where their order is. Aside from order tracking, Manhattan is using the self-service application for returns as well. Consumers can initiate returns using their smart phone or smart speaker, print a return label for shipping an item back, or bring the item to the store. The pre-booked returns give retailers more flexibility with inventory as they have a better idea of what is being returned.
A final take on returns was from Zebra Technologies. Although in this example, it is not individual returns from a customer to a store or DC. Instead, Zebra is using its Smart Pack technology for returns. This technology was generally only used for outbound shipments, but customers have seen the need for it in inbound shipments as well. This covers both direct shipments from manufacturers as well as returns to a DC. The system uses cameras and laser-based 3D modeling of a trailer to give real-time updates on fullness and efficiency. The cameras can also identify problems such as improper loading or delays in loading the trailer. Employees monitor the feeds on a tablet, allowing them to drill down to a specific loading dock door. As boxes are loaded, barcode scans are taken, so specific boxes can be located within trailer if needed.
The other main theme to emerge was machine artificial intelligence and machine learning. Throughout multiple meetings, suppliers discussed and showcased how they are making products smarter. JDA’s big push was the post-acquisition integration of Blue Yonder. Blue Yonder is a leader in machine learning and data science for forecasting and replenishment. The system brings in a stream of over 200 elements that could impact demand, identifying which are the primary influencers and how these influencers interact. These influencers include weather data, social sentiment, events, holidays, promotions, and a host of others. Using this data, JDA can come up with a probabilistic forecast. While retail has been the sweet spot for Blue Yonder, JDA can now expand the solution to other industries.
HighJump is another company that is using data science and artificial intelligence to improve operational efficiencies. The company announced CLASS; their warehouse modeling and simulation tool. CLASS is used in identifying cost efficiencies in the warehouse and as a test platform for introducing operational innovation. It is used for new builds, to test designs before the building process begins, and for operational improvement in an existing site. This is done through a warehouse simulator that builds a digital replica of a customer’s warehouse. Within the simulation, the company can make modifications to the warehouse such as moving racks and rearranging the picking area. Then, the warehouse can run a what if analysis on the changes to see if they will improve efficiency. This all allows companies to test new solutions without having the change the physical layout of the warehouse ahead of time.
It is hard to believe that another NRF Big Show has come and gone. Supply chain and logistics applications felt more present this year than perhaps in years past as retailers and suppliers alike look at the big picture. Artificial intelligence and returns moved to the forefront this year as companies look to become more efficient and get merchandise into selling channels as quickly and profitably as possible. I’ll keep an eye on these themes throughout the year to see how they evolve.